{"cells":[{"cell_type":"code","source":["!pip install datasets transformers torch tqdm pandas huggingface_hub\n","!pip install sentencepiece\n","!pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OdqGQn3M9SHY","executionInfo":{"status":"ok","timestamp":1701664984918,"user_tz":300,"elapsed":40030,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"81baaeb2-02ef-4e6a-bd2e-8649874482ec"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting datasets\n","  Downloading datasets-2.15.0-py3-none-any.whl (521 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n","Requirement 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(1.3.1)\n","Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.11.17)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n","Installing collected packages: pyarrow-hotfix, dill, multiprocess, datasets\n","Successfully installed datasets-2.15.0 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.6\n","Collecting sentencepiece\n","  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: sentencepiece\n","Successfully installed sentencepiece-0.1.99\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","lida 0.0.10 requires kaleido, which is not installed.\n","llmx 0.0.15a0 requires cohere, which is not installed.\n","llmx 0.0.15a0 requires openai, which is not installed.\n","llmx 0.0.15a0 requires tiktoken, which is not installed.\n","tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0m"]}]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":58064,"status":"ok","timestamp":1701665045917,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"},"user_tz":300},"id":"x0LKogzSjJeN","colab":{"base_uri":"https://localhost:8080/"},"outputId":"e1b075e4-8e00-4ee8-f564-8e7cb83b3200"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting wandb\n","  Downloading wandb-0.16.0-py3-none-any.whl (2.1 MB)\n","\u001b[2K     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(1.16.0)\n","Collecting gitdb<5,>=4.0.1 (from GitPython!=3.1.29,>=1.0.0->wandb)\n","  Downloading gitdb-4.0.11-py3-none-any.whl (62 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2023.11.17)\n","Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb)\n","  Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n","Installing collected packages: smmap, setproctitle, sentry-sdk, docker-pycreds, gitdb, GitPython, wandb\n","Successfully installed GitPython-3.1.40 docker-pycreds-0.4.0 gitdb-4.0.11 sentry-sdk-1.38.0 setproctitle-1.3.3 smmap-5.0.1 wandb-0.16.0\n","Collecting bitsandbytes\n","  Downloading bitsandbytes-0.41.2.post2-py3-none-any.whl (92.6 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m17.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: bitsandbytes\n","Successfully installed bitsandbytes-0.41.2.post2\n","Collecting peft\n","  Downloading peft-0.6.2-py3-none-any.whl (174 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m174.7/174.7 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in 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already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.34.0) (2023.11.17)\n","Installing collected packages: huggingface-hub, tokenizers, transformers\n","  Attempting uninstall: huggingface-hub\n","    Found existing installation: huggingface-hub 0.19.4\n","    Uninstalling huggingface-hub-0.19.4:\n","      Successfully uninstalled huggingface-hub-0.19.4\n","  Attempting uninstall: tokenizers\n","    Found existing installation: tokenizers 0.13.3\n","    Uninstalling tokenizers-0.13.3:\n","      Successfully uninstalled tokenizers-0.13.3\n","  Attempting uninstall: transformers\n","    Found existing installation: transformers 4.30.2\n","    Uninstalling transformers-4.30.2:\n","      Successfully uninstalled transformers-4.30.2\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","datasets 2.15.0 requires huggingface-hub>=0.18.0, but you have huggingface-hub 0.17.3 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed huggingface-hub-0.17.3 tokenizers-0.14.1 transformers-4.34.0\n","Collecting loguru\n","  Downloading loguru-0.7.2-py3-none-any.whl (62 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 kB\u001b[0m \u001b[31m691.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: loguru\n","Successfully installed loguru-0.7.2\n","Requirement already satisfied: tokenizers in /usr/local/lib/python3.10/dist-packages (0.14.1)\n","Requirement already satisfied: huggingface_hub<0.18,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from tokenizers) (0.17.3)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<0.18,>=0.16.4->tokenizers) (3.13.1)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<0.18,>=0.16.4->tokenizers) (2023.6.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<0.18,>=0.16.4->tokenizers) (2.31.0)\n","Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<0.18,>=0.16.4->tokenizers) (4.66.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<0.18,>=0.16.4->tokenizers) (6.0.1)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<0.18,>=0.16.4->tokenizers) (4.8.0)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<0.18,>=0.16.4->tokenizers) (23.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub<0.18,>=0.16.4->tokenizers) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub<0.18,>=0.16.4->tokenizers) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub<0.18,>=0.16.4->tokenizers) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub<0.18,>=0.16.4->tokenizers) (2023.11.17)\n","Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.1.99)\n"]}],"source":["# install some necessary packages\n","!pip install wandb\n","!pip install bitsandbytes\n","!pip install peft\n","!pip install datasets\n","!pip install transformers==4.34.0\n","!pip install loguru\n","!pip install tokenizers\n","!pip install sentencepiece"]},{"cell_type":"code","source":["!pip check"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mRwC0GZq94V2","executionInfo":{"status":"ok","timestamp":1701665066746,"user_tz":300,"elapsed":4886,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"3037d542-ff02-41fa-980d-235b60b22c87"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["ipython 7.34.0 requires jedi, which is not installed.\n","lida 0.0.10 requires kaleido, which is not installed.\n","llmx 0.0.15a0 requires cohere, which is not installed.\n","llmx 0.0.15a0 requires openai, which is not installed.\n","llmx 0.0.15a0 requires tiktoken, which is not installed.\n","pygobject 3.42.1 requires pycairo, which is not installed.\n","datasets 2.15.0 has requirement huggingface-hub>=0.18.0, but you have huggingface-hub 0.17.3.\n","tensorflow-probability 0.22.0 has requirement typing-extensions<4.6.0, but you have typing-extensions 4.8.0.\n"]}]},{"cell_type":"code","execution_count":4,"metadata":{"id":"D8AhhjloSE4k","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701665103100,"user_tz":300,"elapsed":13183,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"c8a6900c-770f-45a6-fb45-222d7557df72"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["# connect to google drive\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"Z760YEC5jFul","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701665113985,"user_tz":300,"elapsed":8711,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"917b5410-6ddd-463d-8b34-e33807d23e78"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n","    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n","    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n","    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n","    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n","    \n","    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n","Token: \n","Add token as git credential? (Y/n) n\n","Token is valid (permission: read).\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful\n"]}],"source":["!huggingface-cli login"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"bvgYD54kaxz9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701665118205,"user_tz":300,"elapsed":2021,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"ebdec099-3660-4c59-cd56-f8b446a4ddf6"},"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mbaigzcu123\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"]},{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":6}],"source":["# wandb login\n","import os\n","import wandb\n","\n","# Set the WANDB_API_KEY environment variable\n","os.environ['WANDB_API_KEY'] = '87246aa35c0741ef13381fe3bead47e6d1eeebfb'  # Replace with your actual key\n","\n","# Login to wandb\n","wandb.login()\n"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"G5SD7h8OJQne","executionInfo":{"status":"ok","timestamp":1701665120434,"user_tz":300,"elapsed":99,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}}},"outputs":[],"source":["# import os and sys\n","\n","OUTPUT_DIR = '/content/drive/MyDrive/Hallucination/ChatGLM3_6b_Finance_FT_1'\n","if not os.path.exists(OUTPUT_DIR):\n","    os.makedirs(OUTPUT_DIR)\n","\n","\n","import sys"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"3rd6Z9gRiGRh","executionInfo":{"status":"ok","timestamp":1701665134833,"user_tz":300,"elapsed":11579,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}}},"outputs":[],"source":["from typing import List, Dict, Optional\n","from loguru import logger\n","\n","import torch\n","import torch.nn as nn\n","import bitsandbytes as bnb\n","from datasets import load_dataset\n","import transformers\n","from transformers import (\n","    AutoModel,\n","    AutoModelForCausalLM,\n","    AutoTokenizer,\n","    TrainingArguments,\n","    Trainer,\n","    BitsAndBytesConfig\n",")\n","from peft import (\n","    TaskType,\n","    LoraConfig,\n","    get_peft_model,\n","    prepare_model_for_kbit_training,\n","    prepare_model_for_int8_training,\n","    set_peft_model_state_dict,\n","    get_peft_model_state_dict,\n",")\n","\n","from peft.utils import TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING"]},{"cell_type":"markdown","metadata":{"id":"XElKkj3JZDpd"},"source":["# Set up training hyperparameters"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"ugj3RXjZlxBQ","executionInfo":{"status":"ok","timestamp":1701665137072,"user_tz":300,"elapsed":168,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}}},"outputs":[],"source":["# prepare steps\n","\n","# Set random seed for reproducibility\n","RANDOM_SEED = 3407\n","transformers.set_seed(RANDOM_SEED)\n","\n","# Set up the training hyperparameter\n","MICRO_BATCH_SIZE = 64\n","BATCH_SIZE = 256\n","GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE\n","EPOCHS = 1  # One epoch takes ~6 hours, and 2 epochs may exceed 12-hour.\n","LEARNING_RATE = 1e-4  # Following stanford_alpaca\n","CUTOFF_LEN = 256  # 256 accounts for about 96% of the data. Shorter input, faster training/less VRAM\n","\n","LORA_R = 8  # Some LoRA parameters\n","LORA_ALPHA = 16\n","LORA_DROPOUT = 0.05\n","VAL_SET_SIZE = 2000\n","TARGET_MODULES = TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING['chatglm']\n","\n","# Quantization\n","q_config = BitsAndBytesConfig(load_in_4bit=True,\n","                                bnb_4bit_quant_type='nf4',\n","                                bnb_4bit_use_double_quant=True,\n","                                bnb_4bit_compute_dtype=torch.float16\n","                                )"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"H5dbpwwFhKha","executionInfo":{"status":"ok","timestamp":1701665153256,"user_tz":300,"elapsed":119,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}}},"outputs":[],"source":["# DDP setting\n","device_map = 'auto'\n","world_size = int(os.environ.get('WORLD_SIZE', 1))\n","ddp = (world_size != 1)  # If more than one GPU, then DDP\n","if ddp:\n","    device_map = {'': int(os.environ.get('LOCAL_RANK') or 0)}\n","    GRADIENT_ACCUMULATION_STEPS = GRADIENT_ACCUMULATION_STEPS // world_size"]},{"cell_type":"markdown","metadata":{"id":"clcO8Pxg6IwP"},"source":["# Helper functions"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"Kow7yfXRmTax","executionInfo":{"status":"ok","timestamp":1701665152006,"user_tz":300,"elapsed":137,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}}},"outputs":[],"source":["def print_trainable_parameters(model):\n","    \"\"\"\n","    Prints the number of trainable parameters in the model.\n","    \"\"\"\n","    trainable_params = 0\n","    all_param = 0\n","    for _, param in model.named_parameters():\n","        all_param += param.numel()\n","        if param.requires_grad:\n","            trainable_params += param.numel()\n","    print(\n","        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n","    )"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"jBUkwTSE3Zih","executionInfo":{"status":"ok","timestamp":1701665154890,"user_tz":300,"elapsed":106,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}}},"outputs":[],"source":["def generate_prompt(data_point):\n","    \"\"\"Gen. input text based on a prompt, task instruction, (context info.), and answer\n","\n","    :param data_point: dict: Data point\n","    :return: str: Input text\n","    \"\"\"\n","    # Samples with additional context into.\n","    if data_point['input']:\n","        text = 'Below is an instruction that describes a task, paired with an input that provides' \\\n","               ' further context. Write a response that appropriately completes the request.\\n\\n'\n","        text += f'### Instruction:\\n{data_point[\"instruction\"]}\\n\\n'\n","        text += f'### Input:\\n{data_point[\"input\"]}\\n\\n'\n","        text += f'### Response:\\n{data_point[\"output\"]}'\n","        return text\n","\n","    # Without\n","    else:\n","        text = 'Below is an instruction that describes a task. Write a response that ' \\\n","               'appropriately completes the request.\\n\\n'\n","        text += f'### Instruction:\\n{data_point[\"instruction\"]}\\n\\n'\n","        text += f'### Response:\\n{data_point[\"output\"]}'\n","        return text"]},{"cell_type":"code","execution_count":13,"metadata":{"id":"_xSwms3P3bRf","executionInfo":{"status":"ok","timestamp":1701665155960,"user_tz":300,"elapsed":99,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}}},"outputs":[],"source":["def tokenize(prompt):\n","    \"\"\"Tokenize the input\n","\n","    :param prompt: str: Input text\n","    :return: dict: {'tokenised input text': list, 'mask': list}\n","    \"\"\"\n","    result = tokenizer(prompt, truncation=True, max_length=CUTOFF_LEN + 1, padding='max_length')\n","    return {\n","        'input_ids': result['input_ids'][:-1],\n","        'attention_mask': result['attention_mask'][:-1],\n","    }\n"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"v7_r_fTA4dz6","executionInfo":{"status":"ok","timestamp":1701665156992,"user_tz":300,"elapsed":137,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}}},"outputs":[],"source":["def generate_and_tokenize_prompt(data_point):\n","    \"\"\"This function masks out the labels for the input, so that our loss is computed only on the\n","    response.\"\"\"\n","    if data_point['input']:\n","        user_prompt = 'Below is an instruction that describes a task, paired with an input that ' \\\n","                      'provides further context. Write a response that appropriately completes ' \\\n","                      'the request.\\n\\n'\n","        user_prompt += f'### Instruction:\\n{data_point[\"instruction\"]}\\n\\n'\n","        user_prompt += f'### Input:\\n{data_point[\"input\"]}\\n\\n'\n","        user_prompt += f'### Response:\\n'\n","    else:\n","        user_prompt = 'Below is an instruction that describes a task. Write a response that ' \\\n","                      'appropriately completes the request.'\n","        user_prompt += f'### Instruction:\\n{data_point[\"instruction\"]}\\n\\n'\n","        user_prompt += f'### Response:\\n'\n","\n","    # Count the length of prompt tokens\n","    len_user_prompt_tokens = len(tokenizer(user_prompt,\n","                                           truncation=True,\n","                                           max_length=CUTOFF_LEN + 1,\n","                                           padding='max_length')['input_ids'])\n","    len_user_prompt_tokens -= 1  # Minus 1 (one) for eos token\n","\n","    # Tokenise the input, both prompt and output\n","    full_tokens = tokenizer(\n","        user_prompt + data_point['output'],\n","        truncation=True,\n","        max_length=CUTOFF_LEN + 1,\n","        padding='max_length',\n","    )['input_ids'][:-1]\n","    return {\n","        'input_ids': full_tokens,\n","        'labels': [-100] * len_user_prompt_tokens + full_tokens[len_user_prompt_tokens:],\n","        'attention_mask': [1] * (len(full_tokens)),\n","    }\n"]},{"cell_type":"markdown","metadata":{"id":"UW_rEmd_iJ6Y"},"source":["# Load Model&Tokenizer"]},{"cell_type":"code","execution_count":15,"metadata":{"id":"3-0v7ggZiI1f","colab":{"base_uri":"https://localhost:8080/","height":849,"referenced_widgets":["6bed21e61fe841758212e1b5d464aa73","879e3f7f880148f4a8141224b1a9d5b1","903c7346b457456cb14f9461289689d0","77f98480e2014da28b0d94bde1001b28","1752fd0f6ac7440c9a7309ae73760ed9","1a51533ce7c54cd3963a7169af1fe893","6a6669db3e1e428f8e5da1bd868cd9ba","b92e4ed454a5446db16cfb1972b72c25","1eea2e1a4ccf47b29e09d7a97ce73815","5a9d62b57e6644f9b7603556ac764bc4","e39b968f0cd44b6e8cd0162806b2c4ed","2abc0e609eca46aba9fd493d88dc53ac","d2e0bd69e7734e54a0fd0e2218c324b5","da094d0e18f44a0db5431d8ab6be10d7","5dd9827d96f9417c8e4996be1e626f8b","17efe23acd8f4820b57ec62d2d143f4b","3a8fd04e78954c539d899facad2aaae5","bff5ec9bc4114f21ab845932b490e2c3","4b7d41d332a648d6a45db9e54dc8846c","2e2043600e1c4a0db6c9b6a0f720963c","82d02f2b6ba244f1a805daa271200824","d317a5bf431042da80e2b1cbf04c56f6","f853a8c799ad4013a94e2c4bdb6db6a3","e8b05bfcee85447587242ba968070f9f","ce04905beaba43fcbcf4b0400c086e05","e4b11e04975e453daa700524af4e4749","a48b84851b064656a12015e5c07bdaed","e15fe0b083b24a0c95de72cdc8645cd9","9bc37bc902eb49e386d2da168b413a24","0eb57f16ff1f46b7908d3c1bb9023bf8","eb99e1726808490ca471b6ec879dea5b","295fe9451a82451aa75dca3b95fd3f32","34ef30882fc24421bc1853a481a14a84","0a224ce0f1a945989dcd6c300648d594","db0ec5e59b304c728d4ded4e38ad256f","bec32b78a2124f08befd91263ddff2e8","bd5acda55a944672a0ac32f88ce591a0","fce9e0b88bd2412abc33905edff82287","2debd92e14ca4aa88861e951c268cc05","02afb3b1f92f45e990341ce6d7cbef58","a47a2ba1560345369c820aff606fb783","da397fe63f1c413aab7ee61fd3b6c41b","594987e3689b4de8a6314e759f245e84","da521f02ce8746919e9c6f042225a7a1","d9bf0e44598740b1b1ceaa555e882392","fd359e62c8714c1c9dd7ee8b5c14cc63","cf59cc9381f14827a99d7292b19309bf","f643c534b3e74e01a7a6786b228aedce","f3a4066ebf0b4425a78a2e03e8a7bd1e","31ac4eb92e754c0290d640570949c2d3","5d9bd8c19592416ab75fd33b56e801ee","8702003bc3844630a430f6be5396ce0f","a0aa0447401145a988c8bf7c2a8b8f00","3f98a11962314196be266bf4a187ff67","120fb9cb888e4fa08f920bd1cddad460","1d663f2520d145768a740b3343343b68","0c966d35804e4412a1e8bb284e6bef88","a4bc0e4f80d642a295cb32c6d0e049b0","3da22764a72e4668a6d364c668498736","efb617aa83dc4833a238d8cb747e6b9d","1de5c2fde8b442d2adcf0ac4b4b945c1","4e08547a20bd4f12a3a00cad216dbc46","7e76d4b1c98940c08c85f76f0fab8f28","88933013085d4f3290048246316b0aad","9edc9eebb81d46919f20197bdba540ff","7ef5416c6d3a44248736e045a171ffda","cba17d628610483097c3a6ebe3f3cef8","c3d853049a0a4f3fb11295674e33f51a","2000faac007c471fa95385baedc656e0","a5f694fe01f54fd0bc7e2630552b6852","8c3fe2aa0de84fcf9123490b438a35b0","caec14386bbe46a492880a9b99f19216","5558d1bc76ff48c098c99aafd7697ac9","736f683375954629aee0a884f7684d37","c399b3b71ffc48e9a48b8c4b36a94f9a","8ffbfd0691a840a2a2c068e854a78962","814bccf2b4e645c598d505acbf52caef","101557bd8c034de4912ae46e5ce891ce","d06ac33f0f4045caba09303ff0b7b979","5ebe7f6c4ed74d1789a9e915e11648e4","06e9cab9a899454aa1d2b8ba00de69c2","76070bdf479d49eca2d7f3044b2fba60","8c4d2da16e3d49a6a637511529a3fdd7","dd9e8f0786fa473990e3a4bedc5410e9","be083e80d5d04c4c8105b6e175ad15bb","6f83d5ce7319496cb30f3c45d2640ba7","55b55891eda24b94b2764b9858e999f5","31d44fc7fd15441987bb7b3fc00daa7c","74ad72e315e44779994d38631d95c2bc","6c76a1dad0394c11959960f41d6da729","697a1c7f6d59460da74d5d8b0134e307","307cf209149a4994945d08db9bfd65cb","02d76e463e08423cb6bcb6ec19750577","4f951dd77a9445d9a5caa12d6fe00ee1","c2b09fcb322c451e9fb01c370577b139","66990ab8e4e44febb8e54f87d638c319","d7b285878cef4f68aca64948f01015dc","724190a2910947edbf741b01bc4d4189","f1e5f31040384340909204ee595c887d","bc13f8ce5fe944318e21a99adf155303","9a026da3bea8461992c5d25509fae540","0418366cea6044f5a9fca6a4227a7f6a","8b45d0c867a54f33a23eea32a87faf34","b01d0144779b42dbaa4f956f673d43ed","a7ea3aac179241e59dd45354c8bdaf7a","45f9f51eaeb44ea1a9b76993d415018a","cbf5bacc29c44430b5923b669daa35de","34364223164246fa91e88e9d1f18734c","db14e43e3e214363896d1f0ecd365ad6","713eab7f93b94ab3b8ff5adb15706612","09f2d340c2184fa2b9a45258b7314995","d15e458f63534425931ac8cd85b7e4b7","3246aa7a27964ddab8dded41312ebd70","dfa6cd8e36584e4ead00e161e3c7a6c4","7b54a3a700d14bdd9a5330544023106f","894b47a76ed04fdf9765493cba879a5b","ce629e926c2e4e708a8175a69ce91534","2f23733363b341cd89015569c1905f1e","d87dcda8ba544a9382f0e4c08d6cb41d","8e81a25d2d654b99942b65808e1f0dae","0168b2986a554b6b954c77ccc8814c34","7d2d83718eca45638e1973f417014140","20e29932bb9f4777ae65fa4bb328772c","8a343b53cbe44613a4e3f2da7dea0203","83f5f80573bb42d3a4e407c2b0cf0ddf","9d50b0d65eb8491ab8b2ba27802d935c","1eab0f55a3cc448ab091e5824a32c718","cb09f1d5a29f4f529dbb64cb35cac70d","11eb5bdffe1b4b73b88764927da3eeeb","7dec47268d6a4b7d95915d0b92df91d4","3a3891e01f93463db7079cc684c6e443","0ef5b03bc5964499b6afab382f739eee","3132b95232234a4e99ef079c3de1f638","f07854b4304346e28ef67eb6df4fbb2d","ca30e4a161134eee9295d21d54ce14cd","e96d0d4dbf654004858acea9200aad1d","1e861316ce894861ad70769ddac0e487","631912b5a9cb4f998a063d3e79bb15f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Bai","userId":"07004837890649195177"}},"outputId":"04a85b7f-0c79-4017-e969-9b252b85150e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading tokenizer_config.json:   0%|          | 0.00/244 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6bed21e61fe841758212e1b5d464aa73"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)enization_chatglm.py:   0%|          | 0.00/10.1k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2abc0e609eca46aba9fd493d88dc53ac"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n","- tokenization_chatglm.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading tokenizer.model:   0%|          | 0.00/1.02M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f853a8c799ad4013a94e2c4bdb6db6a3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading config.json:   0%|          | 0.00/1.32k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0a224ce0f1a945989dcd6c300648d594"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)iguration_chatglm.py:   0%|          | 0.00/2.33k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d9bf0e44598740b1b1ceaa555e882392"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n","- configuration_chatglm.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading modeling_chatglm.py:   0%|          | 0.00/54.9k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1d663f2520d145768a740b3343343b68"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading quantization.py:   0%|          | 0.00/14.7k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cba17d628610483097c3a6ebe3f3cef8"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n","- quantization.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n","A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n","- modeling_chatglm.py\n","- quantization.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading (…)model.bin.index.json:   0%|          | 0.00/20.4k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"101557bd8c034de4912ae46e5ce891ce"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading shards:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"74ad72e315e44779994d38631d95c2bc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)l-00001-of-00007.bin:   0%|          | 0.00/1.83G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bc13f8ce5fe944318e21a99adf155303"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)l-00002-of-00007.bin:   0%|          | 0.00/1.97G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"09f2d340c2184fa2b9a45258b7314995"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)l-00003-of-00007.bin:   0%|          | 0.00/1.93G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7d2d83718eca45638e1973f417014140"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)l-00004-of-00007.bin:   0%|          | 0.00/1.82G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3132b95232234a4e99ef079c3de1f638"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)l-00005-of-00007.bin:   0%|          | 0.00/1.97G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8d04c8b9f72d458eb32d44ac3f0c7e8b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)l-00006-of-00007.bin:   0%|          | 0.00/1.93G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f2cedcbca71a4f5a82dfa30a44e36543"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)l-00007-of-00007.bin:   0%|          | 0.00/1.05G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fd9d7840c187400399dc352bc2bd8069"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"816a41d3b114420ba31cef17b1a4084a"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:136: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n","  warnings.warn(\n"]}],"source":["# Load tokenizer & model\n","model_name = \"THUDM/chatglm2-6b\"\n","\n","tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n","\n","model = AutoModel.from_pretrained(\n","        model_name,\n","        quantization_config=q_config,\n","        load_in_8bit = True,\n","        trust_remote_code=True,\n","        device='cuda'\n","    )\n","model = prepare_model_for_int8_training(model, use_gradient_checkpointing=True)"]},{"cell_type":"markdown","metadata":{"id":"UFzWrAeHnpeM"},"source":["# LoRA fine-tune"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"GRZAIkF7nlRh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701665239026,"user_tz":300,"elapsed":240,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"bbbdfe82-4ce3-472d-8275-dc8b0e95ad5a"},"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 1949696 || all params: 3390261248 || trainable%: 0.05750872447219737\n"]}],"source":["# LoRA training\n","lora_config = LoraConfig(\n","    task_type=TaskType.CAUSAL_LM,\n","    inference_mode=False,\n","    r=8,\n","    lora_alpha=32,\n","    lora_dropout=0.1,\n","    target_modules= TARGET_MODULES,\n","    bias='none',\n",")\n","model = get_peft_model(model, lora_config)\n","print_trainable_parameters(model)"]},{"cell_type":"code","execution_count":17,"metadata":{"id":"pJWnX9FuyWHK","executionInfo":{"status":"ok","timestamp":1701665240944,"user_tz":300,"elapsed":111,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}}},"outputs":[],"source":["# this snippet helps us retry and continue training on some checkpoints\n","resume_from_checkpoint = None\n","if resume_from_checkpoint is not None:\n","    checkpoint_name = os.path.join(resume_from_checkpoint, 'pytorch_model.bin')\n","    if not os.path.exists(checkpoint_name):\n","        checkpoint_name = os.path.join(\n","            resume_from_checkpoint, 'adapter_model.bin'\n","        )\n","        resume_from_checkpoint = False\n","    if os.path.exists(checkpoint_name):\n","        logger.info(f'Restarting from {checkpoint_name}')\n","        adapters_weights = torch.load(checkpoint_name)\n","        set_peft_model_state_dict(model, adapters_weights)\n","    else:\n","        logger.info(f'Checkpoint {checkpoint_name} not found')"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"bJCVEI7F2Qai","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701665243745,"user_tz":300,"elapsed":94,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"cf27f871-8f72-4f94-f6e8-1d046c83ebd4"},"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 1949696 || all params: 3390261248 || trainable%: 0.05750872447219737\n"]}],"source":["print_trainable_parameters(model)"]},{"cell_type":"markdown","metadata":{"id":"XVViHXnayKmy"},"source":["# Load Datasets"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"3VsUelhHyMrn","executionInfo":{"status":"ok","timestamp":1701665251426,"user_tz":300,"elapsed":4527,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"colab":{"base_uri":"https://localhost:8080/","height":177,"referenced_widgets":["ac1740240a0e4ebfb0ad8a129c487469","0d3d9d82fc9e4ebfbcd047aec5b7d718","e2b2a2e9c6f649819914e72a9fc2280e","1bae47fd398a4983a9c8d239c3df925d","0308072b2785406284b95dc225945cb6","4558e8061d74448988692bfc3b49731e","d61f7d9349d84a9d8526832e1719760c","416ae1360de14258bb3f492cf4499371","fc4552f007a94897a3fc9290411fa48c","49fc99f0905940b08d9842b0f3d9a032","497028c15bc14884bca4e4ec1e1ea154","6a9fb869af7040c0979348529769db40","639fcc0ff8884b99b802cae55b7138f4","140ba0cb5e464a41a742990a9d6d0864","966afbff8e70429d8c79815ffb8658b8","a8c15f147dc047ccbcd1fa5072838e66","efb25fa59b65471dbe9b97e76e49b2ae","a198ba7b5f664bad9b0d084e72e98ba4","7e5e4cf6e1094960811ae33d3eec58da","3cf60c1f82c646f2afe45a359f26cd17","d33882ca3003417ba569e026634a30d4","77e1d243717640458113dbbce7f86c59","06aed8a01fb14718a5c4bbe660af53d5","ab7b53a1a6dc404d9d077d7da06d9636","368c05beabcd494591e5923b8d76383b","ffccc8cb0c6844f5a19c19dc79ca3d5f","aba8faf92c034448b7b1c0e112d75068","d964dfb6cdfe41bcb5430ddae6796eec","fdd3925d0d0e4e6095f8ec4666b0b378","5d099f604cab4a4e8f1a5c429f1e85ac","34a2d89de576483083e28a2bd11192e6","bf1a9d0a12d14fe0be766d1004aa8502","ca3125bfd32d486fa8bcd0ce7200b484","f23d18a6ef2a47d49593183fec18b955","19934f3b713b4544baad44d2f1cc3b73","eba5f3f69afc4efbb844b2a02d888468","b4134281d87944cfaea7aa61cf8c73d2","87aaa85ae396465fa78a792d0e8291d1","68684bce8e594b3b9b7a611e09aeb9bc","8d383474258f4865b9b66fd3e83a072f","49ba94a8000843659dd05d0b1417cb27","16f0e0527c0a442eaef5e97978653d77","dfb5dee304ae48748f79617813f1c0a1","d83c7d1da96a4e93911c94b903b92481","567410ac82d84adb9f483b6dc702b9a7","d0e9f89e568a48f98c515149a6723e12","c002f8283b9f42e69ea98f0eba4a6872","0255cf629cab494a9b11c0623bbf86c6","23cd1aadd5644b8384a4dbf5255b84b0","db02a9faf4ba474da043c1d8eb573f34","003dee8b0c6f4b81a21321e66019d3da","b6e49adcc6e14428bb90c38924441cbc","3465a2f8c7e4424881a9792f1b474194","a0494a438d70444b9b59da0305363876","6e6c30c07d04434f85f6b2e5da2b9682"]},"outputId":"3a48fd61-7ae6-41c6-a204-f02e98ce707f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/372 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ac1740240a0e4ebfb0ad8a129c487469"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6a9fb869af7040c0979348529769db40"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/31.3M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"06aed8a01fb14718a5c4bbe660af53d5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f23d18a6ef2a47d49593183fec18b955"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"567410ac82d84adb9f483b6dc702b9a7"}},"metadata":{}}],"source":["# Load data\n","data = load_dataset('gbharti/wealth-alpaca_lora')\n","data = data.shuffle(seed=RANDOM_SEED)  # Shuffle dataset here"]},{"cell_type":"code","execution_count":20,"metadata":{"id":"WBoxxEf777bD","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["467214adf3d94184b87e4aac82685c16","416ebbf68056472eaf7f7af04f316535","98c887df2977434286d3e88634d5a0a9","5565168328154bb281d9394a9097ac83","16c40b4308d345d89da08edb1bba4532","269164f04ac74a1db3966b0786be07a4","61c34aec6d084a42b77c0476fefa90b5","6f72b6c74caf4d37b56195ba9b28b36a","e647ec989e6949558b02a63f5c4738e4","654fc443cca2459da59c659dccff4eaf","32a079da82b943c69409d5793185ebbd","6ef0fbbf48e440909e41e48a49aea5b5","ded1563973b448d3bc81e115aecf92eb","66c02f9f2208412fbd4e563a4790e698","f097d8b5428a4a7881939e62b4414848","4185c87b498b42198f7edb89f3462d58","a9e051499b174fa4b8a65fdb9430382b","510d67d1c1ce4b8894d7a7f3a32bec15","b198a781d76240cda9ee8b38aff1e95c","e9f57e8e0fb54f73807b137fee83fcf6","95468784348d44e889c469e491f47b20","67fd3991bcf94ea49e0667987fe7e119"]},"executionInfo":{"status":"ok","timestamp":1701665351078,"user_tz":300,"elapsed":89295,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"76721f4d-56f1-4c03-d34c-8afef466b630"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/42341 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"467214adf3d94184b87e4aac82685c16"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6ef0fbbf48e440909e41e48a49aea5b5"}},"metadata":{}}],"source":["# Train/val split\n","if VAL_SET_SIZE > 0:\n","    train_val = data['train'].train_test_split(\n","        test_size=VAL_SET_SIZE, shuffle=False, seed=RANDOM_SEED\n","    )\n","    train_data = train_val['train'].map(generate_and_tokenize_prompt)\n","    val_data = train_val['test'].map(generate_and_tokenize_prompt)\n","else:\n","    train_data = data['train'].map(generate_and_tokenize_prompt)\n","    val_data = None"]},{"cell_type":"markdown","metadata":{"id":"y9xMUpjx8ETf"},"source":["# Training"]},{"cell_type":"code","execution_count":21,"metadata":{"id":"9h1RMuYF8DFq","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["e6a3e526a03448ad91d5c123b086ab8b","bebfc36e5e5747c5b04af024a2ec57ad","ba0749c1f4da46ed82a70413b490583d","c6543183751f410ebb3f757a9e4105d7","f1298ee14d2547e09538254f846dddd1","dcf52787c9934448b4cc70247f90c6f2","10de9c289f5e4e4db0cab8fdd1f7dfe8","4a064090b9aa43b2bc68b9d87131e8e1"]},"executionInfo":{"status":"ok","timestamp":1701670942560,"user_tz":300,"elapsed":5589575,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"77c03138-faa0-4c49-b42a-d34f66fe34e8"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.16.0"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Run data is saved locally in <code>/content/wandb/run-20231204_044914-lwlkv4qv</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Syncing run <strong><a href='https://wandb.ai/baigzcu123/huggingface/runs/lwlkv4qv' target=\"_blank\">summer-plant-22</a></strong> to <a href='https://wandb.ai/baigzcu123/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[" View project at <a href='https://wandb.ai/baigzcu123/huggingface' target=\"_blank\">https://wandb.ai/baigzcu123/huggingface</a>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[" View run at <a href='https://wandb.ai/baigzcu123/huggingface/runs/lwlkv4qv' target=\"_blank\">https://wandb.ai/baigzcu123/huggingface/runs/lwlkv4qv</a>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='165' max='165' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [165/165 1:32:41, Epoch 0/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>5</td>\n","      <td>4.519200</td>\n","      <td>4.490718</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>4.491000</td>\n","      <td>4.430176</td>\n","    </tr>\n","    <tr>\n","      <td>15</td>\n","      <td>4.364000</td>\n","      <td>4.309227</td>\n","    </tr>\n","    <tr>\n","      <td>20</td>\n","      <td>4.232900</td>\n","      <td>4.141574</td>\n","    </tr>\n","    <tr>\n","      <td>25</td>\n","      <td>4.105200</td>\n","      <td>3.994855</td>\n","    </tr>\n","    <tr>\n","      <td>30</td>\n","      <td>3.923500</td>\n","      <td>3.852665</td>\n","    </tr>\n","    <tr>\n","      <td>35</td>\n","      <td>3.797100</td>\n","      <td>3.690317</td>\n","    </tr>\n","    <tr>\n","      <td>40</td>\n","      <td>3.619400</td>\n","      <td>3.504531</td>\n","    </tr>\n","    <tr>\n","      <td>45</td>\n","      <td>3.428900</td>\n","      <td>3.293380</td>\n","    </tr>\n","    <tr>\n","      <td>50</td>\n","      <td>3.270600</td>\n","      <td>3.069685</td>\n","    </tr>\n","    <tr>\n","      <td>55</td>\n","      <td>3.005400</td>\n","      <td>2.832510</td>\n","    </tr>\n","    <tr>\n","      <td>60</td>\n","      <td>2.796000</td>\n","      <td>2.598847</td>\n","    </tr>\n","    <tr>\n","      <td>65</td>\n","      <td>2.563400</td>\n","      <td>2.434042</td>\n","    </tr>\n","    <tr>\n","      <td>70</td>\n","      <td>2.430300</td>\n","      <td>2.333495</td>\n","    </tr>\n","    <tr>\n","      <td>75</td>\n","      <td>2.354200</td>\n","      <td>2.257771</td>\n","    </tr>\n","    <tr>\n","      <td>80</td>\n","      <td>2.225100</td>\n","      <td>2.195352</td>\n","    </tr>\n","    <tr>\n","      <td>85</td>\n","      <td>2.218400</td>\n","      <td>2.156504</td>\n","    </tr>\n","    <tr>\n","      <td>90</td>\n","      <td>2.156900</td>\n","      <td>2.127849</td>\n","    </tr>\n","    <tr>\n","      <td>95</td>\n","      <td>2.190000</td>\n","      <td>2.102637</td>\n","    </tr>\n","    <tr>\n","      <td>100</td>\n","      <td>2.125100</td>\n","      <td>2.079796</td>\n","    </tr>\n","    <tr>\n","      <td>105</td>\n","      <td>2.138900</td>\n","      <td>2.063292</td>\n","    </tr>\n","    <tr>\n","      <td>110</td>\n","      <td>2.123400</td>\n","      <td>2.049124</td>\n","    </tr>\n","    <tr>\n","      <td>115</td>\n","      <td>2.085200</td>\n","      <td>2.035604</td>\n","    </tr>\n","    <tr>\n","      <td>120</td>\n","      <td>2.101300</td>\n","      <td>2.023546</td>\n","    </tr>\n","    <tr>\n","      <td>125</td>\n","      <td>2.082700</td>\n","      <td>2.011762</td>\n","    </tr>\n","    <tr>\n","      <td>130</td>\n","      <td>2.088800</td>\n","      <td>2.001145</td>\n","    </tr>\n","    <tr>\n","      <td>135</td>\n","      <td>2.006600</td>\n","      <td>1.992661</td>\n","    </tr>\n","    <tr>\n","      <td>140</td>\n","      <td>2.016000</td>\n","      <td>1.986213</td>\n","    </tr>\n","    <tr>\n","      <td>145</td>\n","      <td>2.043400</td>\n","      <td>1.981474</td>\n","    </tr>\n","    <tr>\n","      <td>150</td>\n","      <td>2.009000</td>\n","      <td>1.978259</td>\n","    </tr>\n","    <tr>\n","      <td>155</td>\n","      <td>2.041800</td>\n","      <td>1.976148</td>\n","    </tr>\n","    <tr>\n","      <td>160</td>\n","      <td>2.041900</td>\n","      <td>1.974952</td>\n","    </tr>\n","    <tr>\n","      <td>165</td>\n","      <td>2.019800</td>\n","      <td>1.974528</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["VBox(children=(Label(value='0.002 MB of 0.030 MB uploaded\\r'), FloatProgress(value=0.07275166070516097, max=1.…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e6a3e526a03448ad91d5c123b086ab8b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<style>\n","    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n","    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n","    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n","    </style>\n","<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/loss</td><td>██▇▇▇▆▆▅▅▄▃▃▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eval/runtime</td><td>▅█▆▅█▆▆▇█▆▅█▅▁▇█▆▇▇▇▆▇▇▂▅▆█▇▇█▇▆█</td></tr><tr><td>eval/samples_per_second</td><td>▄▁▃▄▁▃▃▂▁▃▄▁▄█▂▁▃▂▂▂▃▂▂▇▄▃▁▂▂▁▂▃▁</td></tr><tr><td>eval/steps_per_second</td><td>▄▁▂▄▂▃▄▂▁▃▄▂▄█▂▂▃▂▂▂▃▂▂▇▄▄▁▂▂▁▂▃▁</td></tr><tr><td>train/epoch</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇████</td></tr><tr><td>train/global_step</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇████</td></tr><tr><td>train/learning_rate</td><td>▁▂▂▂▃▃▃▄▄▅▅▅▆▆▆▇▇▇██▇▇▆▆▅▅▄▄▃▃▂▂▁</td></tr><tr><td>train/loss</td><td>███▇▇▆▆▅▅▅▄▃▃▂▂▂▂▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/loss</td><td>1.97453</td></tr><tr><td>eval/runtime</td><td>79.2293</td></tr><tr><td>eval/samples_per_second</td><td>25.243</td></tr><tr><td>eval/steps_per_second</td><td>3.155</td></tr><tr><td>train/epoch</td><td>1.0</td></tr><tr><td>train/global_step</td><td>165</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>2.0198</td></tr><tr><td>train/total_flos</td><td>3.879340237180109e+17</td></tr><tr><td>train/train_loss</td><td>2.74592</td></tr><tr><td>train/train_runtime</td><td>5584.9005</td></tr><tr><td>train/train_samples_per_second</td><td>7.581</td></tr><tr><td>train/train_steps_per_second</td><td>0.03</td></tr></table><br/></div></div>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[" View run <strong style=\"color:#cdcd00\">summer-plant-22</strong> at: <a href='https://wandb.ai/baigzcu123/huggingface/runs/lwlkv4qv' target=\"_blank\">https://wandb.ai/baigzcu123/huggingface/runs/lwlkv4qv</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Find logs at: <code>./wandb/run-20231204_044914-lwlkv4qv/logs</code>"]},"metadata":{}}],"source":["# HuggingFace Trainer\n","# Fine-tune the model\n","\n","trainer = transformers.Trainer(\n","    model=model,\n","    train_dataset=train_data,\n","    eval_dataset=val_data,\n","    # trainer argument config\n","    args=transformers.TrainingArguments(\n","        seed=RANDOM_SEED,  # Reproducibility\n","        data_seed=RANDOM_SEED,\n","        per_device_train_batch_size=MICRO_BATCH_SIZE,\n","        gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n","        warmup_steps=100,\n","        num_train_epochs=EPOCHS,\n","        learning_rate=LEARNING_RATE,\n","        fp16=True,\n","        logging_steps=5,\n","        evaluation_strategy='steps' if VAL_SET_SIZE > 0 else 'no',\n","        save_strategy='steps',\n","        save_steps=10,\n","        eval_steps=5 if VAL_SET_SIZE > 0 else None,\n","        output_dir=OUTPUT_DIR,\n","        save_total_limit=3,\n","        load_best_model_at_end=True if VAL_SET_SIZE > 0 else False,\n","        ddp_find_unused_parameters=False if ddp else None,\n","    ),\n","\n","    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",")\n","model.config.use_cache = False\n","\n","# PEFT setup\n","old_state_dict = model.state_dict\n","model.state_dict = (\n","    lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())\n",").__get__(model, type(model))\n","\n","# Use the latest PyTorch 2.0 if possible\n","if torch.__version__ >= '2' and sys.platform != 'win32':\n","    model = torch.compile(model)\n","\n","# Train\n","trainer.train()\n","wandb.finish()"]},{"cell_type":"code","source":["### Merge the LoRA adaptor to Base model\n","\n","# Step 1: Extract LoRA Weights from the Trained Model\n","lora_weights = get_peft_model_state_dict(model)\n","\n","# Step 2: Load the Original ChatGLM2-6b Model\n","original_model = AutoModelForCausalLM.from_pretrained(model_name)\n","\n","# Step 3: Update the Original Model with LoRA Weights\n","for key, value in lora_weights.items():\n","    if key in original_model.state_dict():\n","        original_model.state_dict()[key].data.copy_(value.data)\n","\n","# Step 4: Save the Updated Model\n","# Specify your desired path to save the model\n","updated_model_path = os.path.join(OUTPUT_DIR, \"ChatGLM2-6b_Finance_FT_1_with_lora\")\n","original_model.save_pretrained(updated_model_path)\n","\n","print(\"LoRA parameters have been successfully merged into the original ChatGLM2-6b model.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":230,"referenced_widgets":["6fb4f7dd728744fea6181046aba352af","442b26fb46ae4a5aaa207733ae25f65c","6d7724455cc54877978ed73a388b14a6","381d85e17ca047d0b3bfb023e5deebf1","51f661e1a6634a31aaf4d52c833cbaf7","e03a92ca7e9f46a08dda6bccd2f8397c","e2763c0e59e8458ca2645cc6b4407694","eeeb98b4f30e4d02823330192cc78f1d","d8219a718214450984a5d9fd8c7fc6ab","e5eb7c935b464a438ead71ffdcfa736a","b4c5ecd12b5242e58274cbb05a53bedf"]},"id":"UJXt7XfxR-Y5","executionInfo":{"status":"ok","timestamp":1701671286155,"user_tz":300,"elapsed":51827,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"7b975d97-e649-472d-d3fc-856d5ceecfe4"},"execution_count":23,"outputs":[{"name":"stdout","output_type":"stream","text":["The repository for THUDM/chatglm2-6b contains custom code which must be executed to correctlyload the model. You can inspect the repository content at https://hf.co/THUDM/chatglm2-6b.\n","You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n","\n","Do you wish to run the custom code? [y/N] y\n","The repository for THUDM/chatglm2-6b contains custom code which must be executed to correctlyload the model. You can inspect the repository content at https://hf.co/THUDM/chatglm2-6b.\n","You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n","\n","Do you wish to run the custom code? [y/N] y\n"]},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6fb4f7dd728744fea6181046aba352af"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["LoRA parameters have been successfully merged into the original ChatGLM2-6b model.\n"]}]},{"cell_type":"markdown","metadata":{"id":"zjJBJtorlzoh"},"source":["# push to hub"]},{"cell_type":"code","source":["!git init \"/content/drive/MyDrive/Hallucination/Llama2-7b_Finance_FT_3/git\"\n","!git remote add origin YOUR_REPO_URL\n"],"metadata":{"id":"Jr2yQLAkqbhf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701568023610,"user_tz":300,"elapsed":808,"user":{"displayName":"Gezheng Bai","userId":"07004837890649195177"}},"outputId":"cec2c063-47f7-4548-9537-5eb1db787e09"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[33mhint: Using 'master' as the name for the initial branch. This default branch name\u001b[m\n","\u001b[33mhint: is subject to change. To configure the initial branch name to use in all\u001b[m\n","\u001b[33mhint: of your new repositories, which will suppress this warning, call:\u001b[m\n","\u001b[33mhint: \u001b[m\n","\u001b[33mhint: \tgit config --global init.defaultBranch <name>\u001b[m\n","\u001b[33mhint: \u001b[m\n","\u001b[33mhint: Names commonly chosen instead of 'master' are 'main', 'trunk' and\u001b[m\n","\u001b[33mhint: 'development'. The just-created branch can be renamed via this command:\u001b[m\n","\u001b[33mhint: \u001b[m\n","\u001b[33mhint: \tgit branch -m <name>\u001b[m\n","Initialized empty Git repository in /content/drive/MyDrive/Hallucination/Llama2-7b_Finance_FT_3/git/.git/\n","fatal: not a git repository (or any of the parent directories): .git\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CEpEFeD-h0Ye"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ybLo27HHmfnD"},"outputs":[],"source":["from huggingface_hub import HfApi, Repository\n","\n","model_name = \"Llama2-7b_Finance_lora_1\"\n","model_path = OUTPUT_DIR\n","repo_path = \"/content/drive/MyDrive/Hallucination/Llama2-7b_Finance_FT_1/git\"\n","repo_name = \"Llama2-7b_Finance_lora_1\"  # Replace with your desired repository name\n","\n","# Create a repository on the Hugging Face Hub\n","api = HfApi()\n","# Pass the repo_id argument with the name of the repository\n","repo_url = api.create_repo(token=\"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\", repo_id= repo_name, exist_ok=True)\n","\n","# Clone the repository using the Hugging Face Hub library\n","repo = Repository(local_dir= repo_path, clone_from=repo_url)\n","repo.git_push()\n"]},{"cell_type":"code","source":["!rm -rf repo_path\n","!mkdir repo_path"],"metadata":{"id":"9A7UppByrWV-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","repo = Repository(local_dir=repo_path, clone_from=\"https://huggingface.co/Gason/Llama2-7b_Finance_lora_1\")"],"metadata":{"id":"ytxRlMDwkuf8"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"machine_shape":"hm","provenance":[],"gpuType":"A100","authorship_tag":"ABX9TyM695uGDddyzwIXj70u8Xj+"},"kernelspec":{"display_name":"Python 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